wehub-resource-sync
28 lines
710 B
Python
28 lines
710 B
Python
import shap
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import sklearn
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from sklearn.datasets import load_diabetes
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import mlflow
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# prepare training data
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X, y = load_diabetes(return_X_y=True, as_frame=True)
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# train a model
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model = sklearn.ensemble.RandomForestRegressor(n_estimators=100)
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model.fit(X, y)
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# create an explainer
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explainer_original = shap.Explainer(model.predict, X, algorithm="permutation")
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# log an explainer
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with mlflow.start_run() as run:
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mlflow.shap.log_explainer(explainer_original, artifact_path="shap_explainer")
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# load back the explainer
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explainer_new = mlflow.shap.load_explainer(f"runs:/{run.info.run_id}/shap_explainer")
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# run explainer on data
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shap_values = explainer_new(X[:5])
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print(shap_values)
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